In this case it's not even bad prompts, it's a problem domain ChatGPT wasn't designed to be good at. It's like saying modern medicine is clearly bullshit because a doctor loses a basketball game.

a decent chunk of coding is stupid boilerplate/minutia that varies

...according to a logic, which means LLMs are bad at it.

Ah, you used logic. That's the issue. They don't do that.

I'd say that those details that vary tend not to vary within a language and ecosystem, so a fairly dumb correlative relationship is enough to generally be fine. There's no way to use logic to infer that it's obvious that in language X you need to do mylist.join(string) but in language Y you need to do string.join(mylist), but it's super easy to recognize tokens that suggest those things and a correlation to the vocabulary that matches the context.

Rinse and repeat for things like do I need to specify type and what is the vocabulary for the best type for a numeric value, This variable that makes sense is missing a declaration, does this look to actually be a new distinct variable or just a typo of one that was declared.

But again, I'm thinking mostly in what kind of sort of can work, my experience personally is that it's wrong so often as to be annoying and get in the way of more traditional completion behaviors that play it safe, though with less help particularly for languages like python or javascript.

Yeah, but not when it comes to understanding human speech. There's a reason that repeating words without really understanding them is called parroting. Gray parrots are the smartest and some can actually understand language a little bit, making them smarter than chat, which is just high tech guessing without comprehension

This just in: a hammer makes a poor screwdriver.

LLMs are more like a leaf blower though

I'm impressed, if that's true! In general, an LLM's training cost vs. an LSTM, RNN, or some other more appropriate DNN algorithm suitable for the ruleset is laughably high.

Can i fistfight ChatGPT next? I bet I could kick its ass, too

ChatGPT versus Deepseek is hilarious. They both cheat like crazy and then one side jedi mind tricks the winner into losing.

I imagine the "author" did something like, "Search http://google.scholar.com/ find a publication where AI failed at something and write a paragraph about it."

It's not even as bad as the article claims.

Atari isn't great at chess. https://chess.stackexchange.com/questions/24952/how-strong-is-each-level-of-atari-2600s-video-chess
Random LLMs were nearly as good 2 years ago. https://lmsys.org/blog/2023-05-03-arena/
LLMs that are actually trained for chess have done much better. https://arxiv.org/abs/2501.17186

So they are both masters of troll chess then?

See: King of the Bridge

A lot of writing code is relatively standard patterns and variations on them. For most but the really interesting parts, you could probably write a sufficiently detailed description and get an LLM to produce functional code that does the thing.

Basically for a bunch of common structures and use cases, the logic already exists and is well known and replicated by enough people in enough places in enough languages that an LLM can replicate it well enough, like literally anyone else who has ever written anything in that language.

Oh yes, cost of training are ofc a great loss here, it's not optimized at all, and it's stuck at an average level.

Interestingly, i believe some people did research on it and found some parameters in the model that seemed to represent the state of the chess board (as in, they seem to reflect the current state of the board, and when artificially modified, the model takes modification into account in its playing). It was used by a french youtuber to show how LLMs can somehow have a kinda representation of the world. I can try to get the sources back if you're interested.

Absolutely interested. Thank you for your time to share that.

My career path in neural networks began as a researcher for cancerous tissue object detection in medical diagnostic imaging. Now it is switched to generative models for CAD (architecture, product design, game assets, etc.). I don't really mess about with fine-tuning LLMs.

However, I do self-host my own LLMs as code assistants. Thus, I'm only tangentially involved with the current LLM craze.

But it does interest me, nonetheless!

Well, can it climb trees?

The underlying neural network tech is the same as what the best chess AIs (AlphaZero, Leela) use. The problem is, as you said, that ChatGPT is designed specifically as an LLM so it’s been optimized strictly to write semi-coherent text first, and then any problem solving beyond that is ancillary. Which should say a lot about how inconsistent ChatGPT is at solving problems, given that it’s not actually optimized for any specific use cases.

Wouldn't surprise me if an LLM trained on records of chess moves made good chess moves. I just wouldn't expect the deployed version of ChatGPT to generate coherent chess moves based on the general text it's been trained on.

Yes, I agree wholeheartedly with your clarification.

My career path, as I stated in a different comment in regards to neural networks, is focused on generative DNNs for CAD applications and parametric 3D modeling. Before that, I began as a researcher in cancerous tissue classification and object detection in medical diagnostic imaging.

Thus, large language models are well out of my area of expertise in terms of the architecture of their models.

However, fundamentally it boils down to the fact that the specific large language model used was designed to predict text and not necessarily solve problems/play games to "win"/"survive".

(I admit that I'm just parroting what you stated and maybe rehashing what I stated even before that, but I like repeating and refining in simple terms to practice explaining to laymen and, dare I say, clients. It helps me feel as if I don't come off too pompously when talking about this subject to others; forgive my tedium.)